System

Abstract

A system according to an embodiment is directed to providing a customized diet plan in real-time based on user-specific data.SOLUTION: A system includes a data collection part, an analysis part, a meal plan generation part, and a real-time instruction part. The data collection unit collects data such as the user's metabolic rate, lifestyle, and preferences. The analysis unit analyzes the data collected by the data collection unit. The meal plan generator generates a customized meal plan based on the data analyzed by the analyzer. The real-time guidance unit provides dietary guidance in real time based on the dietary plan generated by the dietary plan generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to provide a customized meal plan based on user-specific data in real time.

[0005] The system of the embodiment aims to provide a customized meal plan in real time based on user-specific data. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a meal plan generation unit, and a real-time instruction unit. The data collection unit collects data such as a user's metabolic rate, lifestyle habits, and preferences. The analysis unit analyzes the data collected by the data collection unit. The meal plan generation unit generates a customized meal plan based on the data analyzed by the analysis unit. The real-time instruction unit provides real-time dietary advice based on the meal plan generated by the meal plan generation unit. [Effects of the Invention]

[0007] In accordance with an embodiment, the system can provide a customized meal plan in real time based on user-specific data. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​meal concierge system according to an embodiment of the present invention provides a customized meal plan based on a user's unique data. This system utilizes a smart glass to log food and a generative AI to perform detailed analysis based on the user's metabolic rate, lifestyle, and preferences, providing real-time dietary advice. This enables the AI ​​meal concierge system to support users' health management, improve the accuracy of demand forecasts in the food and beverage industry, and contribute to reducing food waste.

[0029] The AI ​​meal concierge system according to the embodiment includes a data collection unit, an analysis unit, a meal plan generation unit, and a real-time guidance unit. The data collection unit collects data such as a user's metabolic rate, lifestyle habits, and preferences. For example, the data collection unit uses smart glasses to record meal content and generate a meal log. The data collection unit can also collect data related to the user's metabolic rate and lifestyle habits. The data collection unit can also collect information on the user's preferences and allergies. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs a detailed analysis of the data using a generation AI. The analysis unit can also analyze the data based on the user's metabolic rate and lifestyle habits. The analysis unit can also analyze the data taking into account the user's preferences and allergy information. The meal plan generation unit generates a customized meal plan based on the data analyzed by the analysis unit. For example, the meal plan generation unit suggests optimal meal menus based on the user's metabolic rate and lifestyle habits. The meal plan generation unit can also generate a meal plan taking into account the user's preferences and allergy information. The meal plan generation unit can also generate a meal plan based on the user's health goals. The real-time instruction unit provides real-time dietary advice based on the meal plan generated by the meal plan generation unit. For example, the real-time instruction unit provides dietary advice to the user through smart glasses. The real-time instruction unit can also provide dietary advice using the generation AI. Furthermore, the real-time instruction unit can analyze the user's dietary content in real time and provide specific advice. As a result, the AI ​​meal concierge system according to the embodiment can support users' health management, improve the accuracy of demand forecasts in the food and beverage industry, and contribute to reducing food waste. For example, the output unit displays the scoring results to students and teachers via a web application or mobile application. If students and teachers wish to receive feedback in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending them directly to students and parents.

[0030] In addition to the smart glasses, the data collection unit can also use wearable devices to collect biometric data such as heart rate and body temperature. For example, the data collection unit allows the user to wear a smartwatch and collect biometric data such as heart rate and body temperature in real time. This allows for a detailed record of the body's reactions during meals, and the generation AI analyzes the data based on that data. The data collection unit can also use the smart glasses to record the contents of meals and generate a meal log. Furthermore, the data collection unit can collect data on the user's metabolic rate and lifestyle habits. This allows for a detailed record of the body's reactions during meals, enabling more accurate dietary advice.

[0031] The data collection unit can collect exercise and sleep data in addition to the food log to understand the user's overall health condition. For example, the data collection unit collects exercise and sleep data through a fitness tracker or smartphone app that the user uses daily. This allows the generation AI to understand the user's overall health condition and reflect it in the meal plan. The data collection unit can also record food contents using smart glasses and generate a meal log. Furthermore, the data collection unit can collect data on the user's metabolic rate and lifestyle habits. This allows the user's overall health condition to be understood and a more appropriate meal plan to be provided.

[0032] The data collection unit collects the user's dietary data using voice input, and the generation AI can analyze the voice data. For example, the data collection unit allows the user to voice input the details of their diet into the smart glasses, and the generation AI analyzes the voice data. For example, voice input such as "I ate salad and chicken today" is converted into text data. The data collection unit can also analyze the user's voice in real time and record the details of their diet. Furthermore, the data collection unit can perform highly accurate text conversion using voice recognition technology. This reduces the burden on the user by using voice input, and improves the accuracy of dietary data collection.

[0033] The data collection unit can add a function for sharing with family and friends and integrate and analyze data from multiple users. The data collection unit can add, for example, a function that allows a user to share dietary data with family and friends and integrate and analyze data from multiple users. For example, dietary data for all family members can be managed under a single account. The data collection unit can also share dietary data with friends and jointly manage health. Furthermore, the data collection unit can integrate and analyze data from multiple users to provide more accurate diet plans. In this way, integrating data from multiple users enables more accurate analysis.

[0034] The meal plan generation unit can set long-term health goals based on the user's past meal history and provide a meal plan based on those goals. The meal plan generation unit, for example, analyzes the user's past meal history and builds a system for setting long-term health goals. For example, goals such as weight loss and muscle gain are set based on past meal data. The meal plan generation unit can also provide a meal plan based on the user's health goals. Furthermore, the meal plan generation unit can also provide a meal plan that takes nutritional balance and calorie restriction into consideration based on the user's past meal history. This makes it possible to provide a meal plan based on the user's long-term health goals.

[0035] The meal plan generation unit generates a meal plan according to the season and weather, and can support the user's health management. The meal plan generation unit builds a system that provides the user with an optimal meal plan based on, for example, season and weather data. For example, it suggests cold ingredients in the summer and hot ingredients in the winter. The meal plan generation unit can also provide a meal plan according to the weather. For example, it suggests hot soup on a rainy day. Furthermore, the meal plan generation unit can provide a meal plan that takes into account nutritional balance according to the season and weather. This makes it possible to provide a meal plan according to the season and weather and support the user's health management.

[0036] The meal plan generation unit can include recipe videos and cooking procedures in the user's meal plan to support actual cooking. For example, the meal plan generation unit can add recipe videos to the user's meal plan and build a system that visually supports cooking procedures. For example, recipe videos can be displayed on smart glasses so that the user can refer to them while cooking. The meal plan generation unit can also provide step-by-step cooking procedures. For example, cooking time and necessary tools can be displayed. Furthermore, the meal plan generation unit can provide a list of necessary ingredients based on the user's meal plan. This supports the user in cooking efficiently.

[0037] The meal plan generation unit can automatically generate an ingredient shopping list for the meal plan and link with online shopping. The meal plan generation unit, for example, builds a system that automatically generates a shopping list of necessary ingredients based on the user's meal plan. For example, it lists ingredients needed for a meal menu and provides it to the user. The meal plan generation unit can also link with online shopping to support the user in easily purchasing ingredients. For example, it can automatically send the shopping list to an online shopping site, allowing the user to purchase ingredients with one click. Furthermore, the meal plan generation unit can check ingredient inventory in real time and efficiently purchase the necessary ingredients. This supports the user in easily purchasing ingredients.

[0038] The real-time guidance unit can analyze the nutritional balance of meals in real time and provide advice on how to make up for any missing nutrients. The real-time guidance unit can, for example, analyze meal content in real time through smart glasses and build a system to evaluate nutritional balance. For example, if there is a vitamin or mineral deficiency, it can suggest ingredients to supplement it. The real-time guidance unit can also analyze the user's meal content in real time and provide specific advice. For example, if there is a protein deficiency, it can suggest ingredients that are high in protein. Furthermore, the real-time guidance unit can analyze the user's nutritional balance in real time and reflect this in the meal plan. This allows the user's nutritional balance to be analyzed in real time and appropriate advice to be provided.

[0039] The real-time instruction unit can introduce a voice assistant into the dietary guidance and provide audio advice to the user. The real-time instruction unit, for example, uses a voice assistant to build a system that provides real-time advice to the user while they are eating. For example, audio advice such as "Eat more slowly" is provided. The real-time instruction unit can also analyze the user's dietary content in real time and provide specific audio advice. For example, advice such as "This meal is high in calories, so have a lighter meal next time." Furthermore, the real-time instruction unit can use the voice assistant to provide advice based on the user's meal plan. This allows for audio advice to be provided to the user, enabling effective dietary guidance.

[0040] The real-time instruction unit can notify the user of the content of the dietary advice to their smartphone and provide post-meal follow-up. The real-time instruction unit, for example, builds a system that notifies the user of the content of the dietary advice to their smartphone and provides post-meal follow-up. For example, it sends a nutritional balance report to the smartphone after a meal. The real-time instruction unit can also analyze the user's meal content in real time and provide post-meal follow-up. For example, it provides advice on the next meal after a meal. Furthermore, the real-time instruction unit can send notifications to the user's smartphone to support post-meal health management. This allows post-meal follow-up for the user to improve the effectiveness of the dietary advice.

[0041] The data collection unit can provide data not only to the food and beverage industry, but also to health insurance companies and the fitness industry, allowing for the creation of a wide range of business models. For example, the data collection unit can provide the collected data to health insurance companies and use it to optimize insurance plans. For example, this can be used to set insurance premiums based on health conditions and promote preventive medicine. The data collection unit can also provide data to the fitness industry and use it to optimize fitness plans. For example, it can provide individually optimized fitness plans based on the user's exercise data. Furthermore, the data collection unit can also provide data to the food and beverage industry to improve the accuracy of demand forecasts. This allows for the creation of a wide range of business models by providing data to multiple industries.

[0042] The data collection unit can support the development of new products and the formulation of marketing strategies based on the results of data analysis. For example, the data collection unit can support food manufacturers in developing new products based on the results of data analysis. For example, the data collection unit can suggest products for users who are deficient in specific nutrients. The data collection unit can also support the formulation of marketing strategies. For example, the data collection unit can suggest the selection of target markets and promotion plans. Furthermore, the data collection unit can also suggest products that suit consumer preferences based on the results of data analysis. In this way, the data collection unit can support the development of new products and the formulation of marketing strategies based on the results of data analysis.

[0043] The data collection unit can analyze regional dietary trends based on the data and propose region-specific marketing strategies. The data collection unit, for example, analyzes regional dietary data and builds a system that proposes region-specific marketing strategies. For example, a promotion can be carried out based on a menu that is popular in a particular region. The data collection unit can also analyze regional dietary trends and propose a marketing strategy that takes into account the ingredients and food culture unique to the region. Furthermore, the data collection unit can analyze regional consumption patterns and provide region-specific marketing strategies. This makes it possible to analyze regional dietary trends and propose region-specific marketing strategies.

[0044] The data collection unit can use the data to support restaurant menu development and suggest menus that suit consumer preferences. The data collection unit, for example, builds a system that uses the collected data to suggest menus that suit consumer preferences to restaurants. For example, it analyzes trends in preferences for specific ingredients and reflects this in the menu. The data collection unit can also support menu development based on consumer preferences. For example, it can suggest health-oriented menus. Furthermore, the data collection unit can also suggest menus that suit the season or trends based on consumer preference data. This makes it possible to support restaurant menu development and suggest menus that suit consumer preferences.

[0045] The data collection unit can use the data to support restaurant menu development and suggest menus that suit consumer preferences. The data collection unit, for example, builds a system that uses the collected data to suggest menus that suit consumer preferences to restaurants. For example, it analyzes trends in preferences for specific ingredients and reflects this in the menu. The data collection unit can also support menu development based on consumer preferences. For example, it can suggest health-oriented menus. Furthermore, the data collection unit can also suggest menus that suit the season or trends based on consumer preference data. This makes it possible to support restaurant menu development and suggest menus that suit consumer preferences.

[0046] The data collection unit works in conjunction with the restaurant's inventory management system to grasp the inventory status in real time and prevent unnecessary purchases. The data collection unit, for example, works in conjunction with the restaurant's inventory management system to build a system that grasps the inventory status in real time. For example, it automatically orders ingredients that are running low on stock. The data collection unit can also update inventory data in real time to prevent unnecessary purchases. Furthermore, the data collection unit can also work in conjunction with the inventory management system to perform efficient inventory management. This allows the restaurant's inventory status to be grasped in real time and prevent unnecessary purchases.

[0047] The data collection department can provide educational programs for reducing food waste and raise awareness among restaurant staff. The data collection department, for example, builds a system that provides educational programs for reducing food waste to restaurant staff. For example, the data collection department can educate restaurant staff about the importance of reducing food waste through online courses or workshops. The data collection department can also raise awareness among restaurant staff through the educational programs. Furthermore, the data collection department can include specific methods for reducing food waste in the educational programs. This can raise awareness among restaurant staff and contribute to reducing food waste.

[0048] The data collection unit can develop an app for reducing food waste and provide advice to consumers on how to reduce food waste at home. The data collection unit can, for example, develop an app that provides advice to consumers on how to reduce food waste at home. For example, the app can suggest managing ingredients in the refrigerator and prioritizing the use of ingredients that are close to their expiration date. The data collection unit can also provide consumers with specific methods for reducing food waste through the app. Furthermore, the data collection unit can support consumers in managing their ingredients and provide advice on how to reduce food waste. This makes it possible to provide advice to consumers on how to reduce food waste at home.

[0049] The data collection unit can work with restaurants to build a system for donating surplus food ingredients to local food banks. The data collection unit, for example, works with restaurants to develop a system for building a system for donating surplus food ingredients to local food banks. For example, it provides information about surplus food ingredients to food banks in real time. The data collection unit can also work with food banks to efficiently donate food ingredients. Furthermore, the data collection unit can manage surplus food ingredients at restaurants and donate them to food banks when necessary. This makes it possible to work with restaurants to build a system for donating surplus food ingredients to local food banks.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] In addition to collecting the user's dietary data, the data collection unit can also collect data on the user's hobbies and interests. For example, if the user is interested in a particular sport or activity, the meal plan can be customized based on that information. The data collection unit can also collect and analyze dietary data when the user participates in events or activities related to the user's hobbies. Furthermore, the data collection unit can suggest specific ingredients and dishes based on the user's hobbies and interests. This makes it possible to provide a meal plan tailored to the user's lifestyle.

[0052] In addition to collecting the user's dietary data, the data collection unit can also collect data on the user's social connections. For example, the data collection unit can record how often the user eats with friends and family and the contents of the meals they eat. The data collection unit can also customize meal plans based on the user's social connections. For example, if the user often eats with family, the data collection unit can suggest menus that the whole family can enjoy. Furthermore, the data collection unit can suggest meal times and locations based on the user's social connections. This makes it possible to provide meal plans that take the user's social connections into consideration.

[0053] In addition to collecting the user's dietary data, the data collection unit can also collect data related to the user's travels and business trips. For example, the user can record the meals he or she eats at his or her travel destinations and his or her eating patterns during business trips. The data collection unit can also customize meal plans based on the user's travels and business trips. For example, the data collection unit can suggest ingredients and dishes specific to the region of the travel destination. Furthermore, the data collection unit can suggest meal timings and locations based on the user's travels and business trips. This makes it possible to provide a meal plan that takes the user's travels and business trips into consideration.

[0054] The data collection unit can customize a meal plan based on the user's exercise data in addition to the user's dietary data. For example, the data collection unit can record the meals the user eats after exercising and adjust the meal plan based on that data. The data collection unit can also suggest meal timings based on the user's exercise data. For example, it can suggest recovery meals after exercise. Furthermore, the data collection unit can adjust the nutritional balance of meals based on the user's exercise data. This makes it possible to provide a meal plan that takes the user's exercise data into consideration.

[0055] The data collection unit can customize a meal plan based on the user's sleep data in addition to the user's dietary data. For example, the data collection unit can record the user's sleep patterns and adjust the meal plan based on that data. The data collection unit can also suggest meal timings based on the user's sleep data. For example, the data collection unit can suggest meals that promote good quality sleep. Furthermore, the data collection unit can adjust the nutritional balance of meals based on the user's sleep data. This makes it possible to provide a meal plan that takes the user's sleep data into consideration.

[0056] The data collection unit can customize a meal plan based on the user's health data in addition to the user's dietary data. For example, it records the user's health data, such as blood pressure and blood sugar levels, and adjusts the meal plan based on that data. The data collection unit can also adjust the nutritional balance of meals based on the user's health data. For example, if the user's blood pressure is high, it can suggest a low-salt diet. Furthermore, the data collection unit can also suggest meal timings based on the user's health data. This makes it possible to provide a meal plan that takes the user's health data into consideration.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The data collection unit collects data such as the user's metabolic rate, lifestyle habits, and preferences. For example, the data collection unit may use smart glasses to record the contents of meals and generate a food log. The data collection unit may also collect data on the user's metabolic rate and lifestyle habits. Furthermore, the data collection unit may also collect information on the user's preferences and allergies. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses generative AI to perform a detailed analysis of the data. The analysis unit can also analyze the data based on the user's metabolic rate and lifestyle habits. Furthermore, the analysis unit can analyze the data taking into account the user's preferences and allergy information. Step 3: The meal plan generation unit generates a customized meal plan based on the data analyzed by the analysis unit. For example, the meal plan generation unit suggests an optimal meal menu based on the user's metabolic rate and lifestyle habits. The meal plan generation unit can also generate a meal plan taking into account the user's preferences and allergy information. Furthermore, the meal plan generation unit can also generate a meal plan based on the user's health goals. Step 4: The real-time instruction unit provides real-time dietary advice based on the diet plan generated by the diet plan generation unit. For example, the real-time instruction unit provides dietary advice to the user through smart glasses. The real-time instruction unit can also provide dietary advice using a generation AI. Furthermore, the real-time instruction unit can analyze the user's dietary content in real time and provide specific advice.

[0059] (Example 2) The AI ​​meal concierge system according to an embodiment of the present invention provides a customized meal plan based on a user's unique data. This system utilizes a smart glass to log food and a generative AI to perform detailed analysis based on the user's metabolic rate, lifestyle, and preferences, providing real-time dietary advice. This enables the AI ​​meal concierge system to support users' health management, improve the accuracy of demand forecasts in the food and beverage industry, and contribute to reducing food waste.

[0060] The AI ​​meal concierge system according to the embodiment includes a data collection unit, an analysis unit, a meal plan generation unit, and a real-time guidance unit. The data collection unit collects data such as a user's metabolic rate, lifestyle habits, and preferences. For example, the data collection unit uses smart glasses to record meal content and generate a meal log. The data collection unit can also collect data related to the user's metabolic rate and lifestyle habits. The data collection unit can also collect information on the user's preferences and allergies. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs a detailed analysis of the data using a generation AI. The analysis unit can also analyze the data based on the user's metabolic rate and lifestyle habits. The analysis unit can also analyze the data taking into account the user's preferences and allergy information. The meal plan generation unit generates a customized meal plan based on the data analyzed by the analysis unit. For example, the meal plan generation unit suggests optimal meal menus based on the user's metabolic rate and lifestyle habits. The meal plan generation unit can also generate a meal plan taking into account the user's preferences and allergy information. The meal plan generation unit can also generate a meal plan based on the user's health goals. The real-time instruction unit provides real-time dietary advice based on the meal plan generated by the meal plan generation unit. For example, the real-time instruction unit provides dietary advice to the user through smart glasses. The real-time instruction unit can also provide dietary advice using the generation AI. Furthermore, the real-time instruction unit can analyze the user's dietary content in real time and provide specific advice. As a result, the AI ​​meal concierge system according to the embodiment can support users' health management, improve the accuracy of demand forecasts in the food and beverage industry, and contribute to reducing food waste. For example, the output unit displays the scoring results to students and teachers via a web application or mobile application. If students and teachers wish to receive feedback in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending them directly to students and parents.

[0061] In addition to the smart glasses, the data collection unit can also use wearable devices to collect biometric data such as heart rate and body temperature. For example, the data collection unit allows the user to wear a smartwatch and collect biometric data such as heart rate and body temperature in real time. This allows for a detailed record of the body's reactions during meals, and the generation AI analyzes the data based on that data. The data collection unit can also use the smart glasses to record the contents of meals and generate a meal log. Furthermore, the data collection unit can collect data on the user's metabolic rate and lifestyle habits. This allows for a detailed record of the body's reactions during meals, enabling more accurate dietary advice.

[0062] The data collection unit can collect exercise and sleep data in addition to the food log to understand the user's overall health condition. For example, the data collection unit collects exercise and sleep data through a fitness tracker or smartphone app that the user uses daily. This allows the generation AI to understand the user's overall health condition and reflect it in the meal plan. The data collection unit can also record food contents using smart glasses and generate a meal log. Furthermore, the data collection unit can collect data on the user's metabolic rate and lifestyle habits. This allows the user's overall health condition to be understood and a more appropriate meal plan to be provided.

[0063] The data collection unit can use the emotion estimation function to estimate the user's emotions while eating in real time and collect data based on emotions. The data collection unit, for example, uses a camera in the smart glasses to analyze the user's facial expressions and estimate the user's emotions while eating in real time. For example, an emotion score is calculated based on facial expressions such as smiling or furrowing the brow. The data collection unit can also analyze the user's voice to estimate emotions. For example, the tone and speed of voice can be analyzed to calculate an emotion score. Furthermore, the data collection unit can collect the user's biometric data (heart rate and electrodermal activity) and analyze emotions using an emotion estimation algorithm. For example, an emotion score can be calculated based on heart rate fluctuations. This makes it possible to provide a meal plan that matches the user's emotional state.

[0064] The data collection unit collects the user's dietary data using voice input, and the generation AI can analyze the voice data. For example, the data collection unit allows the user to voice input the details of their diet into the smart glasses, and the generation AI analyzes the voice data. For example, voice input such as "I ate salad and chicken today" is converted into text data. The data collection unit can also analyze the user's voice in real time and record the details of their diet. Furthermore, the data collection unit can perform highly accurate text conversion using voice recognition technology. This reduces the burden on the user by using voice input, and improves the accuracy of dietary data collection.

[0065] The data collection unit can add a function for sharing with family and friends and integrate and analyze data from multiple users. The data collection unit can add, for example, a function that allows a user to share dietary data with family and friends and integrate and analyze data from multiple users. For example, dietary data for all family members can be managed under a single account. The data collection unit can also share dietary data with friends and jointly manage health. Furthermore, the data collection unit can integrate and analyze data from multiple users to provide more accurate diet plans. In this way, integrating data from multiple users enables more accurate analysis.

[0066] The meal plan generation unit can set long-term health goals based on the user's past meal history and provide a meal plan based on those goals. The meal plan generation unit, for example, analyzes the user's past meal history and builds a system for setting long-term health goals. For example, goals such as weight loss and muscle gain are set based on past meal data. The meal plan generation unit can also provide a meal plan based on the user's health goals. Furthermore, the meal plan generation unit can also provide a meal plan that takes nutritional balance and calorie restriction into consideration based on the user's past meal history. This makes it possible to provide a meal plan based on the user's long-term health goals.

[0067] The meal plan generation unit generates a meal plan according to the season and weather, and can support the user's health management. The meal plan generation unit builds a system that provides the user with an optimal meal plan based on, for example, season and weather data. For example, it suggests cold ingredients in the summer and hot ingredients in the winter. The meal plan generation unit can also provide a meal plan according to the weather. For example, it suggests hot soup on a rainy day. Furthermore, the meal plan generation unit can provide a meal plan that takes into account nutritional balance according to the season and weather. This makes it possible to provide a meal plan according to the season and weather and support the user's health management.

[0068] The meal plan generation unit uses the emotion estimation function to suggest a meal menu that corresponds to the user's emotional state, thereby reducing stress and improving mood. The meal plan generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and suggest a meal menu that corresponds to the user's emotional state. For example, when stress is high, ingredients that have a relaxing effect are suggested. The meal plan generation unit can also provide a meal menu based on the user's emotional state. For example, when feeling depressed, ingredients that will improve mood are suggested. Furthermore, the meal plan generation unit can also use the emotion estimation function to provide a meal menu that takes into account nutritional balance according to the user's emotional state. This makes it possible to suggest a meal menu that corresponds to the user's emotional state, thereby reducing stress and improving mood.

[0069] The meal plan generation unit can include recipe videos and cooking procedures in the user's meal plan to support actual cooking. For example, the meal plan generation unit can add recipe videos to the user's meal plan and build a system that visually supports cooking procedures. For example, recipe videos can be displayed on smart glasses so that the user can refer to them while cooking. The meal plan generation unit can also provide step-by-step cooking procedures. For example, cooking time and necessary tools can be displayed. Furthermore, the meal plan generation unit can provide a list of necessary ingredients based on the user's meal plan. This supports the user in cooking efficiently.

[0070] The meal plan generation unit can automatically generate an ingredient shopping list for the meal plan and link with online shopping. The meal plan generation unit, for example, builds a system that automatically generates a shopping list of necessary ingredients based on the user's meal plan. For example, it lists ingredients needed for a meal menu and provides it to the user. The meal plan generation unit can also link with online shopping to support the user in easily purchasing ingredients. For example, it can automatically send the shopping list to an online shopping site, allowing the user to purchase ingredients with one click. Furthermore, the meal plan generation unit can check ingredient inventory in real time and efficiently purchase the necessary ingredients. This supports the user in easily purchasing ingredients.

[0071] The meal plan generation unit can use the emotion estimation function to select ingredients based on the user's emotions and provide a meal plan that matches the emotions. The meal plan generation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional state and selects ingredients based on that. For example, when stress is high, ingredients that have a relaxing effect are selected. The meal plan generation unit can also provide a meal plan based on the user's emotional state. For example, when feeling depressed, ingredients that will improve mood are selected. Furthermore, the meal plan generation unit can use the emotion estimation function to select ingredients that take into account nutritional balance according to the user's emotional state. This makes it possible to provide a meal plan that matches the user's emotions.

[0072] The real-time guidance unit can analyze the nutritional balance of meals in real time and provide advice on how to make up for any missing nutrients. The real-time guidance unit can, for example, analyze meal content in real time through smart glasses and build a system to evaluate nutritional balance. For example, if there is a vitamin or mineral deficiency, it can suggest ingredients to supplement it. The real-time guidance unit can also analyze the user's meal content in real time and provide specific advice. For example, if there is a protein deficiency, it can suggest ingredients that are high in protein. Furthermore, the real-time guidance unit can analyze the user's nutritional balance in real time and reflect this in the meal plan. This allows the user's nutritional balance to be analyzed in real time and appropriate advice to be provided.

[0073] The real-time instruction unit can use the emotion estimation function to analyze the user's emotions while eating and provide dietary advice based on those emotions. The real-time instruction unit, for example, uses a camera in smart glasses to analyze the user's facial expressions while eating and builds a system that estimates emotions in real time. For example, if the user is feeling stressed, the real-time instruction unit can suggest foods that have a relaxing effect. The real-time instruction unit can also analyze the user's voice to estimate emotions. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the real-time instruction unit can collect the user's biometric data (heart rate and electrodermal activity) and analyze emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide dietary advice based on the user's emotions.

[0074] The real-time instruction unit can introduce a voice assistant into the dietary guidance and provide audio advice to the user. The real-time instruction unit, for example, uses a voice assistant to build a system that provides real-time advice to the user while they are eating. For example, audio advice such as "Eat more slowly" is provided. The real-time instruction unit can also analyze the user's dietary content in real time and provide specific audio advice. For example, advice such as "This meal is high in calories, so have a lighter meal next time." Furthermore, the real-time instruction unit can use the voice assistant to provide advice based on the user's meal plan. This allows for audio advice to be provided to the user, enabling effective dietary guidance.

[0075] The real-time instruction unit can notify the user of the content of the dietary advice to their smartphone and provide post-meal follow-up. The real-time instruction unit, for example, builds a system that notifies the user of the content of the dietary advice to their smartphone and provides post-meal follow-up. For example, it sends a nutritional balance report to the smartphone after a meal. The real-time instruction unit can also analyze the user's meal content in real time and provide post-meal follow-up. For example, it provides advice on the next meal after a meal. Furthermore, the real-time instruction unit can send notifications to the user's smartphone to support post-meal health management. This allows post-meal follow-up for the user to improve the effectiveness of the dietary advice.

[0076] The real-time instruction unit can use the emotion estimation function to suggest relaxation methods after a meal based on the user's emotions. For example, the real-time instruction unit uses the emotion estimation function to analyze the user's emotional state after a meal and build a system that suggests relaxation methods based on that. For example, if the user is highly stressed, the real-time instruction unit can suggest music that has a relaxing effect. The real-time instruction unit can also provide relaxation methods based on the user's emotional state. For example, if the user is feeling depressed, the real-time instruction unit can suggest methods to improve the user's mood. Furthermore, the real-time instruction unit can use the emotion estimation function to provide relaxation methods according to the user's emotional state. This makes it possible to suggest relaxation methods based on the user's emotions and support relaxation after a meal.

[0077] The data collection unit can provide data not only to the food and beverage industry, but also to health insurance companies and the fitness industry, allowing for the creation of a wide range of business models. For example, the data collection unit can provide the collected data to health insurance companies and use it to optimize insurance plans. For example, this can be used to set insurance premiums based on health conditions and promote preventive medicine. The data collection unit can also provide data to the fitness industry and use it to optimize fitness plans. For example, it can provide individually optimized fitness plans based on the user's exercise data. Furthermore, the data collection unit can also provide data to the food and beverage industry to improve the accuracy of demand forecasts. This allows for the creation of a wide range of business models by providing data to multiple industries.

[0078] The data collection unit can support the development of new products and the formulation of marketing strategies based on the results of data analysis. For example, the data collection unit can support food manufacturers in developing new products based on the results of data analysis. For example, the data collection unit can suggest products for users who are deficient in specific nutrients. The data collection unit can also support the formulation of marketing strategies. For example, the data collection unit can suggest the selection of target markets and promotion plans. Furthermore, the data collection unit can also suggest products that suit consumer preferences based on the results of data analysis. In this way, the data collection unit can support the development of new products and the formulation of marketing strategies based on the results of data analysis.

[0079] The data collection unit can use the emotion estimation function to perform demand prediction based on consumer emotions, thereby providing more accurate predictions. The data collection unit, for example, uses the emotion estimation function to analyze consumer emotion data and build a system that performs demand predictions based on the data. For example, a prediction is provided that high-calorie meals are preferred during times of strong positive emotions. The data collection unit can also perform demand predictions based on the consumer's emotional state. For example, a prediction is provided that health-oriented meals are preferred during times of high stress. Furthermore, the data collection unit can also use the emotion estimation function to perform demand predictions based on consumer emotion data. This allows demand predictions to be performed based on consumer emotions, thereby providing more accurate predictions.

[0080] The data collection unit can analyze regional dietary trends based on the data and propose region-specific marketing strategies. The data collection unit, for example, analyzes regional dietary data and builds a system that proposes region-specific marketing strategies. For example, a promotion can be carried out based on a menu that is popular in a particular region. The data collection unit can also analyze regional dietary trends and propose a marketing strategy that takes into account the ingredients and food culture unique to the region. Furthermore, the data collection unit can analyze regional consumption patterns and provide region-specific marketing strategies. This makes it possible to analyze regional dietary trends and propose region-specific marketing strategies.

[0081] The data collection unit can use the data to support restaurant menu development and suggest menus that suit consumer preferences. The data collection unit, for example, builds a system that uses the collected data to suggest menus that suit consumer preferences to restaurants. For example, it analyzes trends in preferences for specific ingredients and reflects this in the menu. The data collection unit can also support menu development based on consumer preferences. For example, it can suggest health-oriented menus. Furthermore, the data collection unit can also suggest menus that suit the season or trends based on consumer preference data. This makes it possible to support restaurant menu development and suggest menus that suit consumer preferences.

[0082] The data collection unit can use the data to support restaurant menu development and suggest menus that suit consumer preferences. The data collection unit, for example, builds a system that uses the collected data to suggest menus that suit consumer preferences to restaurants. For example, it analyzes trends in preferences for specific ingredients and reflects this in the menu. The data collection unit can also support menu development based on consumer preferences. For example, it can suggest health-oriented menus. Furthermore, the data collection unit can also suggest menus that suit the season or trends based on consumer preference data. This makes it possible to support restaurant menu development and suggest menus that suit consumer preferences.

[0083] The data collection unit can use the emotion estimation function to develop an advertising campaign based on consumer emotions, thereby achieving effective marketing. The data collection unit, for example, uses the emotion estimation function to analyze consumer emotion data and build a system that develops an advertising campaign based on the data. For example, a specific product is promoted during times when positive emotions are strong. The data collection unit can also develop an advertising campaign based on the consumer's emotional state. For example, a product with a relaxing effect is promoted during times of high stress. Furthermore, the data collection unit can also use the emotion estimation function to develop an advertising campaign based on consumer emotion data. This allows an advertising campaign based on consumer emotions to be developed, thereby achieving effective marketing.

[0084] The data collection unit works in conjunction with the restaurant's inventory management system to grasp the inventory status in real time and prevent unnecessary purchases. The data collection unit, for example, works in conjunction with the restaurant's inventory management system to build a system that grasps the inventory status in real time. For example, it automatically orders ingredients that are running low on stock. The data collection unit can also update inventory data in real time to prevent unnecessary purchases. Furthermore, the data collection unit can also work in conjunction with the inventory management system to perform efficient inventory management. This allows the restaurant's inventory status to be grasped in real time and prevent unnecessary purchases.

[0085] The data collection department can provide educational programs for reducing food waste and raise awareness among restaurant staff. The data collection department, for example, builds a system that provides educational programs for reducing food waste to restaurant staff. For example, the data collection department can educate restaurant staff about the importance of reducing food waste through online courses or workshops. The data collection department can also raise awareness among restaurant staff through the educational programs. Furthermore, the data collection department can include specific methods for reducing food waste in the educational programs. This can raise awareness among restaurant staff and contribute to reducing food waste.

[0086] The data collection unit can develop an app for reducing food waste and provide advice to consumers on how to reduce food waste at home. The data collection unit can, for example, develop an app that provides advice to consumers on how to reduce food waste at home. For example, the app can suggest managing ingredients in the refrigerator and prioritizing the use of ingredients that are close to their expiration date. The data collection unit can also provide consumers with specific methods for reducing food waste through the app. Furthermore, the data collection unit can support consumers in managing their ingredients and provide advice on how to reduce food waste. This makes it possible to provide advice to consumers on how to reduce food waste at home.

[0087] The data collection unit can work with restaurants to build a system for donating surplus food ingredients to local food banks. The data collection unit, for example, works with restaurants to develop a system for building a system for donating surplus food ingredients to local food banks. For example, it provides information about surplus food ingredients to food banks in real time. The data collection unit can also work with food banks to efficiently donate food ingredients. Furthermore, the data collection unit can manage surplus food ingredients at restaurants and donate them to food banks when necessary. This makes it possible to work with restaurants to build a system for donating surplus food ingredients to local food banks.

[0088] The data collection unit can use the emotion estimation function to conduct awareness activities for reducing food waste based on consumer emotions and raise awareness throughout society. The data collection unit, for example, uses the emotion estimation function to analyze consumer emotion data and build a system for conducting awareness activities for reducing food waste based on that data. For example, the data collection unit can emphasize awareness messages during times when positive emotions are strong. The data collection unit can also conduct awareness activities based on consumers' emotional state. For example, the data collection unit can emphasize the importance of reducing food waste during times when stress is low. Furthermore, the data collection unit can use the emotion estimation function to conduct awareness activities based on consumer emotion data. This allows awareness activities for reducing food waste based on consumer emotions to raise awareness throughout society.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] In addition to collecting the user's dietary data, the data collection unit can also collect data on the user's hobbies and interests. For example, if the user is interested in a particular sport or activity, the meal plan can be customized based on that information. The data collection unit can also collect and analyze dietary data when the user participates in events or activities related to the user's hobbies. Furthermore, the data collection unit can suggest specific ingredients and dishes based on the user's hobbies and interests. This makes it possible to provide a meal plan tailored to the user's lifestyle.

[0091] In addition to collecting the user's dietary data, the data collection unit can also collect data on the user's social connections. For example, the data collection unit can record how often the user eats with friends and family and the contents of the meals they eat. The data collection unit can also customize meal plans based on the user's social connections. For example, if the user often eats with family, the data collection unit can suggest menus that the whole family can enjoy. Furthermore, the data collection unit can suggest meal times and locations based on the user's social connections. This makes it possible to provide meal plans that take the user's social connections into consideration.

[0092] In addition to collecting the user's dietary data, the data collection unit can also collect data related to the user's travels and business trips. For example, the user can record the meals he or she eats at his or her travel destinations and his or her eating patterns during business trips. The data collection unit can also customize meal plans based on the user's travels and business trips. For example, the data collection unit can suggest ingredients and dishes specific to the region of the travel destination. Furthermore, the data collection unit can suggest meal timings and locations based on the user's travels and business trips. This makes it possible to provide a meal plan that takes the user's travels and business trips into consideration.

[0093] The data collection unit can use the emotion estimation function to suggest meal times based on the user's emotional state. For example, if the user is feeling stressed, it can suggest eating at a time when the user can relax. The data collection unit can also adjust the frequency of meals based on the user's emotional state. For example, if the user is feeling depressed, it can suggest eating small meals frequently. Furthermore, the data collection unit can also suggest places to eat based on the user's emotional state. For example, it can suggest eating in a relaxing environment. This makes it possible to suggest meal times according to the user's emotional state.

[0094] The data collection unit can use the emotion estimation function to adjust the amount of food eaten based on the user's emotional state. For example, if the user is feeling stressed, it can suggest a small amount of food. The data collection unit can also adjust the calories of the food eaten based on the user's emotional state. For example, if the user is feeling depressed, it can suggest a low-calorie meal. Furthermore, the data collection unit can also adjust the nutritional balance of the food eaten based on the user's emotional state. For example, it can suggest a meal that is rich in nutrients that have a relaxing effect. This makes it possible to adjust the amount of food eaten according to the user's emotional state.

[0095] The data collection unit can use the emotion estimation function to suggest a dining environment based on the user's emotional state. For example, if the user is feeling stressed, it can suggest a dining environment in a quiet environment. The data collection unit can also suggest a dining location based on the user's emotional state. For example, it can suggest a dining location in nature. Furthermore, the data collection unit can adjust the dining atmosphere based on the user's emotional state. For example, it can suggest playing relaxing music. This makes it possible to suggest a dining environment that suits the user's emotional state.

[0096] The data collection unit can customize a meal plan based on the user's exercise data in addition to the user's dietary data. For example, the data collection unit can record the meals the user eats after exercising and adjust the meal plan based on that data. The data collection unit can also suggest meal timings based on the user's exercise data. For example, it can suggest recovery meals after exercise. Furthermore, the data collection unit can adjust the nutritional balance of meals based on the user's exercise data. This makes it possible to provide a meal plan that takes the user's exercise data into consideration.

[0097] The data collection unit can customize a meal plan based on the user's sleep data in addition to the user's dietary data. For example, the data collection unit can record the user's sleep patterns and adjust the meal plan based on that data. The data collection unit can also suggest meal timings based on the user's sleep data. For example, the data collection unit can suggest meals that promote good quality sleep. Furthermore, the data collection unit can adjust the nutritional balance of meals based on the user's sleep data. This makes it possible to provide a meal plan that takes the user's sleep data into consideration.

[0098] The data collection unit can use the emotion estimation function to suggest types of meals based on the user's emotional state. For example, if the user is feeling stressed, it can suggest dishes that use ingredients that have a relaxing effect. The data collection unit can also suggest cooking methods for meals based on the user's emotional state. For example, it can suggest dishes that are easy to prepare. Furthermore, the data collection unit can also suggest presentations for meals based on the user's emotional state. For example, it can suggest dishes that look beautiful. This makes it possible to suggest types of meals that suit the user's emotional state.

[0099] The data collection unit can customize a meal plan based on the user's health data in addition to the user's dietary data. For example, it records the user's health data, such as blood pressure and blood sugar levels, and adjusts the meal plan based on that data. The data collection unit can also adjust the nutritional balance of meals based on the user's health data. For example, if the user's blood pressure is high, it can suggest a low-salt diet. Furthermore, the data collection unit can also suggest meal timings based on the user's health data. This makes it possible to provide a meal plan that takes the user's health data into consideration.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The data collection unit collects data such as the user's metabolic rate, lifestyle habits, and preferences. For example, the data collection unit may use smart glasses to record the contents of meals and generate a food log. The data collection unit may also collect data on the user's metabolic rate and lifestyle habits. Furthermore, the data collection unit may also collect information on the user's preferences and allergies. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses generative AI to perform a detailed analysis of the data. The analysis unit can also analyze the data based on the user's metabolic rate and lifestyle habits. Furthermore, the analysis unit can analyze the data taking into account the user's preferences and allergy information. Step 3: The meal plan generation unit generates a customized meal plan based on the data analyzed by the analysis unit. For example, the meal plan generation unit suggests an optimal meal menu based on the user's metabolic rate and lifestyle habits. The meal plan generation unit can also generate a meal plan taking into account the user's preferences and allergy information. Furthermore, the meal plan generation unit can also generate a meal plan based on the user's health goals. Step 4: The real-time instruction unit provides real-time dietary advice based on the diet plan generated by the diet plan generation unit. For example, the real-time instruction unit provides dietary advice to the user through smart glasses. The real-time instruction unit can also provide dietary advice using a generation AI. Furthermore, the real-time instruction unit can analyze the user's dietary content in real time and provide specific advice.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects data such as a user's metabolic rate, lifestyle habits, and preferences; an analysis unit that analyzes the data collected by the data collection unit; a meal plan generator that generates a customized meal plan based on the data analyzed by the analyzer; a real-time instruction unit that provides dietary advice in real time based on the diet plan generated by the diet plan generation unit; A system characterized by:

2. The data collection unit In addition to smart glasses, wearable devices will also be used to collect biometric data such as heart rate and body temperature.

2. The system of claim 1.

3. The data collection unit When collecting the user's dietary data, the dietary details are recorded using voice input, and the generation AI analyzes the voice data.

2. The system of claim 1.

4. The meal plan generation unit Proposing a meal menu according to the user's emotional state to reduce stress and improve mood 2. The system of claim 1.

5. The real-time leadership unit Analyzing the user's emotions during a meal and providing dietary advice according to the emotions 2. The system of claim 1.

6. The data collection unit We will provide this data not only to the food and beverage industry, but also to health insurance companies and the fitness industry, and build a wide range of business models.

2. The system of claim 1.

7. The data collection unit Collaborate with restaurants to create a system for donating surplus food to local food banks 2. The system of claim 1.

8. The data collection unit Conduct educational activities to reduce food waste based on consumer sentiment and raise awareness throughout society.

2. The system of claim 1.

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